# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

/kaggle/input/dlmia-hw05-test/train.csv
/kaggle/input/dlmia-hw05-test/test.csv
/kaggle/input/dlmia-hw05-test/test_images/029.jpg
/kaggle/input/dlmia-hw05-test/test_images/014.jpg
/kaggle/input/dlmia-hw05-test/test_images/109.jpg
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/kaggle/input/dlmia-hw05-test/test_images/044.jpg
/kaggle/input/dlmia-hw05-test/test_images/040.jpg
/kaggle/input/dlmia-hw05-new/train.json
/kaggle/input/dlmia-hw05-new/train.csv
/kaggle/input/dlmia-hw05-new/test.csv
/kaggle/input/dlmia-hw05-new/val.json
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/A0_0.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/10_2 (2).jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/A0_1.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/220 (2).jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/10_2.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/10_1 (2).jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/A0_5.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/A0_3.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/10_1.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/10_4.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/A0_6.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/4440_2.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/220_1.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/10_0 (2).jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/A0.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/4440_0.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/10_3 (2).jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/220_6.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/4440.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/220.jpg
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/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/220_4.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/10 (2).jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/A0_2.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/4440_1.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/220_5.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/10_3.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/4440_4.jpg
/kaggle/input/dlmia-hw05-new/intercostal_pleural_thickening/10_0.jpg
/kaggle/input/dlmia-hw05-new/lung_field_infiltration/FILE0_9.jpg
/kaggle/input/dlmia-hw05-new/lung_field_infiltration/10_6 (2).jpg
/kaggle/input/dlmia-hw05-new/lung_field_infiltration/10_2 (2).jpg
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/kaggle/input/dlmia-hw05-new/lung_field_infiltration/10.jpg
/kaggle/input/dlmia-hw05-new/lung_field_infiltration/220_2.jpg
/kaggle/input/dlmia-hw05-new/lung_field_infiltration/FILE0_19.jpg
/kaggle/input/dlmia-hw05-new/lung_field_infiltration/Volume0_2.jpg
/kaggle/input/dlmia-hw05-new/lung_field_infiltration/FILE0_20.jpg
/kaggle/input/dlmia-hw05-new/lung_field_infiltration/10_5.jpg
/kaggle/input/dlmia-hw05-new/lung_field_infiltration/10_2.jpg
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/kaggle/input/dlmia-hw05-new/lung_field_infiltration/FILE0_2.jpg
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/kaggle/input/dlmia-hw05-new/cardiac_hypertrophy/Volume0_1.jpg

import os
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
import random
import numpy as np
import pandas as pd
import math
from tqdm.notebook import tqdm

# visualization
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.patches as patches

# PyTorch
import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision import models
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection.rpn import AnchorGenerator
from torchvision.models.detection import FasterRCNN
from torchvision.transforms import v2
from torchvision import tv_tensors
from torchvision.tv_tensors import BoundingBoxes

# object detection
!pip install pycocotools
import pycocotools
from pycocotools.coco import COCO

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!wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/engine.py
!wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/utils.py
!wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/coco_utils.py
!wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/coco_eval.py
!wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/transforms.py
    
from engine import evaluate

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## TODO: Prepare your own information
class config:
    
    ## roots for training & valid
    root = "/kaggle/input/dlmia-hw05-new"
    info_root = "/kaggle/input/dlmia-hw05-new"
    save_root = "/kaggle/working/"

    ## for test images
    test_root = '/kaggle/input/dlmia-hw05-test'
    info_root_test = '/kaggle/input/dlmia-hw05-test'
    
    num_classes = 8 #(for fasterrcnn: background + # of classes): 1+7=8
    
    
    batch_size = 4        
    epochs = 18           
    weight_decay = 1e-4   
    lr = 3e-3             
    momentum = 0.9        
    seed = 42             
    workers = 4           
    
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def seed_everything(seed):
   
    random.seed(seed) # Set Python random seed    
    np.random.seed(seed) # Set NumPy random seed    
    torch.manual_seed(seed) # Set PyTorch random seed for CPU and GPU
    if torch.cuda.is_available():
        torch.cuda.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)
    
    # Set PyTorch deterministic operations for cudnn backend
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

    
seed_everything(config.seed)

annfile = config.info_root + "/train.json"
coco = COCO(annfile)
coco.cats

loading annotations into memory...
Done (t=0.02s)
creating index...
index created!

{1: {'id': 1, 'name': 'aortic_curvature'},
 2: {'id': 2, 'name': 'aortic_atherosclerosis_calcification'},
 3: {'id': 3, 'name': 'cardiac_hypertrophy'},
 4: {'id': 4, 'name': 'intercostal_pleural_thickening'},
 5: {'id': 5, 'name': 'lung_field_infiltration'},
 6: {'id': 6, 'name': 'degenerative_joint_disease_of_the_thoracic_spine'},
 7: {'id': 7, 'name': 'scoliosis'}}

coco.loadImgs(0)

[{'file_name': 'normal/220_94.jpg', 'height': 2560, 'width': 2472, 'id': 0}]

ann_ids = coco.getAnnIds(imgIds = 0)
coco.loadAnns(ann_ids)

[]

ann_ids = coco.getAnnIds(imgIds = 155)
coco.loadAnns(ann_ids)

[{'id': 163,
  'image_id': 155,
  'category_id': 3,
  'bbox': [816, 936, 1048, 896],
  'area': 939008,
  'iscrowd': 0,
  'segmentation': [[816, 936, 1864, 936, 1864, 1832, 816, 1832]]},
 {'id': 164,
  'image_id': 155,
  'category_id': 5,
  'bbox': [144, 336, 2008, 1941],
  'area': 3897528,
  'iscrowd': 0,
  'segmentation': [[144, 336, 2152, 336, 2152, 2277, 144, 2277]]}]

del coco

import torch
from torchvision.transforms import v2
from torchvision import tv_tensors

class medTransform:
    def __init__(self, train=False):
        if train:
            
            self.transforms = v2.Compose(
                [
                    v2.ToImage(),                  
                    v2.RandomRotation(degrees=5),  
                    v2.ColorJitter(
                        brightness=0.2,            
                        contrast=0.2,              
                        saturation=0.0,            
                        hue=0.0
                    ),
                    v2.ToDtype(torch.float32, scale=True),  
                ]
            )
        else:
            self.transforms = v2.Compose(
                [
                    v2.ToImage(),
                    v2.ToDtype(torch.float32, scale=True),
                ]
            )

    def __call__(self, x, bboxes):
        
        if isinstance(x, torch.Tensor):
            
            height, width = x.shape[-2], x.shape[-1]
        else:
            
            width, height = x.size

        
        bboxes = tv_tensors.BoundingBoxes(
            bboxes,
            format="XYWH",
            canvas_size=(height, width)
        )
        
        return self.transforms(x, bboxes)

class MedDataset(Dataset):
    
    def __init__(self, root, info_root, split, transforms = None):
        self.split = split
        self.root = root
        self.info_root = info_root
        self.transforms = transforms
        self.coco = COCO(os.path.join(self.info_root, f"{self.split}.json"))
        self.ids = list(sorted(self.coco.imgs.keys()))
    
    def get_image(self, img_id: int):
        image_path = os.path.join(self.root,self.coco.loadImgs(img_id)[0]['file_name'])
        image = Image.open(image_path).convert("RGB")
        return image

    def get_annotation(self, img_id: int):        
        return self.coco.loadAnns(self.coco.getAnnIds(img_id))
          
    def __getitem__(self, index):
        normal = False
        img_id = self.ids[index]
        image = self.get_image(img_id) 
        annotation = self.get_annotation(img_id)
        
        bboxes = [a['bbox']  for a in annotation]
        category_ids = [a['category_id']  for a in annotation]
        if bboxes == []:
            normal = True     
            
        if self.transforms:
            image, bboxes = self.transforms(image, bboxes)     
            
        def reformat_bboxes(boxes):
            return [[val[0], val[1], val[0] + val[2], val[1] + val[3]] for val in boxes]
        
        if normal != True:
            ## Recall that the original format is COCO
            bboxes = reformat_bboxes(bboxes)   

        def create_target(bboxes, normal):
            if normal:
                return {
                    'boxes': torch.zeros((0, 4), dtype=torch.float32),  # Empty boxes
                    'labels': torch.tensor(category_ids, dtype=torch.int64),
                    'image_id': img_id,
                    'area': torch.zeros((0,), dtype=torch.float32),  # Empty areas
                    'iscrowd': torch.zeros((0,), dtype=torch.int64),  # Empty tensor for iscrowd
                }
            else:
            
                return {
                    'boxes': torch.tensor(bboxes),
                    'labels': torch.tensor(category_ids, dtype=torch.int64),
                    'image_id': img_id,
                    'area': torch.tensor([(bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) for bbox in bboxes], dtype=torch.float32),
                    'iscrowd': torch.tensor([a['iscrowd'] for a in annotation], dtype=torch.int64)
                }
       
        targets = create_target(bboxes,normal)
        return image, targets
        
    def __len__(self):
        return len(self.ids)

def collate_fn(batch: list[torch.tensor, dict]):
    return tuple(zip(*batch))

def plot_image_with_boxes(image_tensor, boxes_dict):
    image_np = image_tensor.permute(1, 2, 0).numpy()
    fig, ax = plt.subplots(1)
    # Display the image
    ax.imshow(image_np)
    for box in boxes_dict['boxes']:
        # Extract coordinates (x0, y0, x1, y1)
        x0, y0, x1, y1 = box
        # Calculate the height as (y0 - y1) since y0 is the top and y1 is the bottom
        height = y1 - y0
        # Create a rectangle patch with (x0, y0) as the top-left corner
        rect = patches.Rectangle((x0, y0), x1 - x0, height, linewidth=2, edgecolor='r', facecolor='none')
        ax.add_patch(rect)
    plt.show()

train_dataset = MedDataset(root = config.root, info_root = config.info_root, split = "train", transforms = medTransform(train=True))
train_loader = DataLoader(train_dataset, batch_size = config.batch_size, shuffle = True,num_workers=4, collate_fn = collate_fn)

loading annotations into memory...
Done (t=0.01s)
creating index...
index created!

a,b = train_dataset.__getitem__(1)
plot_image_with_boxes(a,b)

a,b = train_dataset.__getitem__(155)
print(b)
plot_image_with_boxes(a,b)

{'boxes': tensor([[ 790,  922, 1875, 1861],
        [ 100,  294, 2188, 2318]]), 'labels': tensor([3, 5]), 'image_id': 155, 'area': tensor([1018815., 4226112.]), 'iscrowd': tensor([0, 0])}

def fasterrcnn(num_classes):
    model = models.detection.fasterrcnn_resnet50_fpn(weights='COCO_V1') 
    in_features = model.roi_heads.box_predictor.cls_score.in_features
    model.roi_heads.box_predictor = None
    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
    
    return model

model = fasterrcnn(config.num_classes)

Downloading: "https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth" to /root/.cache/torch/hub/checkpoints/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth
100%|██████████| 160M/160M [00:00<00:00, 192MB/s]

def train_one_epoch(model, train_loader, optimizer, epoch, device):
    model.train()
    
    train_loss = []
    train_loss_dict = []
    
    lr_scheduler = None

    for images, targets in tqdm(train_loader):
        images = [image.to(device) for image in images]
        targets = [{k: (torch.tensor(v,device=device) if not isinstance(v, torch.Tensor) else v.to(device)) for k, v in t.items()} for t in targets]

        loss_dict = model(images, targets)
        losses = sum(loss for loss in loss_dict.values())

        batch_loss_value = losses.item()
        batch_loss_dict = {k: v.item() for k, v in loss_dict.items()}

        train_loss.append(batch_loss_value)
        train_loss_dict.append(batch_loss_dict)
      
        optimizer.zero_grad()
        losses.backward()
        optimizer.step()
    
        if lr_scheduler is not None:
            lr_scheduler.step()
        
    train_loss = np.mean(train_loss)    
    train_loss_dict = pd.DataFrame(train_loss_dict).mean()
    train_loss_classifier = train_loss_dict.loss_classifier
    train_loss_box_reg = train_loss_dict.loss_box_reg
    train_loss_rpn_box_reg = train_loss_dict.loss_rpn_box_reg
    train_loss_objectness = train_loss_dict.loss_objectness

    return train_loss, train_loss_classifier, train_loss_box_reg, train_loss_rpn_box_reg, train_loss_objectness

def validation(model, val_loader, device):
    model.train()
    #model.eval()
    for m in model.modules():
        if isinstance(m, torchvision.ops.Conv2dNormActivation):
            m.eval()
        if isinstance(m, torchvision.ops.FrozenBatchNorm2d):
            m.eval()
        if isinstance(m, torch.nn.BatchNorm2d):
            m.eval()
    val_loss = []
    val_loss_dict = []
    with torch.no_grad():
        for images, targets in tqdm(val_loader):
            images = [image.to(device) for image in images]
            targets = [{k: (torch.tensor(v,device=device) if not isinstance(v, torch.Tensor) else v.to(device)) for k, v in t.items()} for t in targets]

            loss = model(images, targets)
            total_loss = sum(l for l in loss.values())
            
            loss_value = total_loss.item()
            loss_dict = {k: v.item() for k, v in loss.items()}
            
            val_loss.append(loss_value)
            val_loss_dict.append(loss_dict)
    
    val_loss = np.mean(val_loss)
    
    val_loss_dict = pd.DataFrame(val_loss_dict).mean()
    val_loss_classifier = val_loss_dict.loss_classifier
    val_loss_box_reg = val_loss_dict.loss_box_reg
    val_loss_rpn_box_reg = val_loss_dict.loss_rpn_box_reg
    val_loss_objectness = val_loss_dict.loss_objectness
    
    return val_loss, val_loss_classifier, val_loss_box_reg, val_loss_rpn_box_reg, val_loss_objectness

del model
del train_dataset, train_loader

def seed_worker(worker_id):
    worker_seed = torch.initial_seed() % 2**32
    np.random.seed(worker_seed)
    random.seed(worker_seed)

def main():
    
    seed_everything(config.seed)    
    g = torch.Generator()
    g.manual_seed(config.seed)
    
    train_dataset = MedDataset(root = config.root, info_root = config.info_root, split = "train", transforms = medTransform(train=True))
    val_dataset = MedDataset(root = config.root, info_root = config.info_root, split = "val",  transforms = medTransform(train=False))

    train_loader = DataLoader(train_dataset, batch_size = config.batch_size, shuffle = True,
                              num_workers=config.workers, collate_fn = collate_fn,pin_memory=True
                             )
    val_loader = DataLoader(val_dataset, batch_size = config.batch_size, shuffle = False,
                            num_workers=config.workers, worker_init_fn=seed_worker,
                            generator=g, collate_fn = collate_fn,pin_memory=True
                           )

    
    device = config.device
    model =  fasterrcnn(num_classes = config.num_classes)
    model.to(device)
    
    parameters = [p for p in model.parameters() if p.requires_grad]

    optimizer = torch.optim.SGD(parameters, lr = config.lr, momentum = config.momentum, nesterov = True, weight_decay = config.weight_decay)
    best_val_loss = float("inf")
    best_map50 = 0.0
    history = {
        "train": {
            "loss": [],
            "loss_classifier": [],
            "loss_box_reg": [],
            "loss_rpn_box_reg": [],
            "loss_objectness": []
        },
        "val": {
            "loss": [],
            "loss_classifier": [],
            "loss_box_reg": [],
            "loss_rpn_box_reg": [],
            "loss_objectness": []
        },
        "map50":{
            "train":[],
            "valid":[],
        }
    }
    best_idx = 0
    print('start')
    for epoch in range(config.epochs):
        print()
        train_loss, train_loss_classifier, train_loss_box_reg, train_loss_rpn_box_reg, train_loss_objectness = train_one_epoch(
            model, train_loader, optimizer, epoch, device,
        )
        
        val_loss, val_loss_classifier, val_loss_box_reg, val_loss_rpn_box_reg, val_loss_objectness = validation(
            model, val_loader, device
        )

        ## Training
        history["train"]["loss"].append(train_loss)
        history["train"]["loss_classifier"].append(train_loss_classifier)
        history["train"]["loss_box_reg"].append(train_loss_box_reg)
        history["train"]["loss_rpn_box_reg"].append(train_loss_rpn_box_reg)
        history["train"]["loss_objectness"].append(train_loss_objectness)
        ## Validation
        history["val"]["loss"].append(val_loss)
        history["val"]["loss_classifier"].append(val_loss_classifier)
        history["val"]["loss_box_reg"].append(val_loss_box_reg)
        history["val"]["loss_rpn_box_reg"].append(val_loss_rpn_box_reg)
        history["val"]["loss_objectness"].append(val_loss_objectness)

        
        print(f'Epoch: {epoch+1}/{config.epochs} | LR: {optimizer.state_dict()["param_groups"][0]["lr"]:.6f}')

        print("*****Training*****")
        print(f'Loss: {train_loss:.4f} | Classifier Loss: {train_loss_classifier:.4f} | Box Reg Loss: {train_loss_box_reg:.4f} | RPN Box Reg Loss: {train_loss_rpn_box_reg:.4f} | Objectness Loss: {train_loss_objectness:.4f}')
        train_evaluator = evaluate(model, train_loader, device = device)
        print("*****Validation*****")
        print(f'Loss: {val_loss:.4f} | Classifier Loss: {val_loss_classifier:.4f} | Box Reg Loss: {val_loss_box_reg:.4f} | RPN Box Reg Loss: {val_loss_rpn_box_reg:.4f} | Objectness Loss: {val_loss_objectness:.4f}')
        valid_evaluator = evaluate(model, val_loader, device = device)
        
        train_map50 = train_evaluator.coco_eval['bbox'].stats[1]
        valid_map50 = valid_evaluator.coco_eval['bbox'].stats[1]
        
        history["map50"]["train"].append(train_map50)   
        history["map50"]["valid"].append(valid_map50)        

        ## TODO save your model

        if valid_map50 > best_map50:
            best_map50 = valid_map50
            save_file = {
                "model": model.state_dict(),
                "optimizer": optimizer.state_dict(),
                "epoch": epoch,
                "args": config
            }
            best_idx=epoch
            
            torch.save(save_file, os.path.join(config.save_root,"final.pth"))
        
    print(f'Best epoch in {best_idx+1}') 


    ## Evaluation result 
    plt.figure(figsize = (12, 5))
    plt.subplot(1, 2, 1)
    plt.plot(range(config.epochs), history["map50"]["train"], label = 'Training map50')
    plt.plot(range(config.epochs), history["map50"]["valid"], label = 'Validation map50')
    plt.xlabel('Epoch')
    plt.ylabel('map')
    plt.legend()
    plt.title('Validation and Testing map50')
    plt.show()
    
    plt.figure(figsize = (12, 5))
    plt.subplot(1, 2, 1)
    plt.plot(range(config.epochs), history["train"]["loss"], label = 'Training Loss')
    plt.plot(range(config.epochs), history["val"]["loss"], label = 'Validation Loss')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.legend()
    plt.title('Training and Validation Loss Curves')
    plt.show()
        
    plt.figure(figsize = (12, 5))
    plt.subplot(1, 2, 1)
    plt.plot(range(config.epochs), history["train"]["loss_classifier"], label = 'Training Classifier Loss')
    plt.plot(range(config.epochs), history["val"]["loss_classifier"], label = 'Validation Classifier Loss')
    plt.xlabel('Epoch')
    plt.ylabel('Classifier Loss')
    plt.legend()
    plt.title('Training and Validation Classifier Loss Curves')
    plt.show()
        
    plt.figure(figsize = (12, 5))
    plt.subplot(1, 2, 1)
    plt.plot(range(config.epochs), history["train"]["loss_box_reg"], label = 'Training Box Reg Loss')
    plt.plot(range(config.epochs), history["val"]["loss_box_reg"], label = 'Validation Box Reg Loss')
    plt.xlabel('Epoch')
    plt.ylabel('Box Reg Loss')
    plt.legend()
    plt.title('Training and Validation Box Reg Loss Curves')
    plt.show()
        
    plt.figure(figsize = (12, 5))
    plt.subplot(1, 2, 1)
    plt.plot(range(config.epochs), history["train"]["loss_rpn_box_reg"], label = 'Training RPN Box Reg Loss')
    plt.plot(range(config.epochs), history["val"]["loss_rpn_box_reg"], label = 'Validation RPN Box Reg Loss')
    
    plt.xlabel('Epoch')
    plt.ylabel('RPN Box Reg Loss')
    plt.legend()
    plt.title('Training and Validation RPN Box Reg Loss Curves')
    plt.show()
        
    plt.figure(figsize = (12, 5))
    plt.subplot(1, 2, 1)
    plt.plot(range(config.epochs), history["train"]["loss_objectness"], label = 'Training Objectness Loss')
    plt.plot(range(config.epochs), history["val"]["loss_objectness"], label = 'Validation Objectness Loss')

    plt.xlabel('Epoch')
    plt.ylabel('Objectness Loss')
    plt.legend()
    plt.title('Training and Validation Objectness Loss Curves')
    plt.show()

if __name__ == "__main__":
    main()

loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
start

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Epoch: 1/18 | LR: 0.003000
*****Training*****
Loss: 0.2848 | Classifier Loss: 0.1648 | Box Reg Loss: 0.0993 | RPN Box Reg Loss: 0.0088 | Objectness Loss: 0.0119
creating index...
index created!
Test:  [ 0/70]  eta: 0:08:10  model_time: 0.3441 (0.3441)  evaluator_time: 0.0465 (0.0465)  time: 7.0115  data: 6.5975  max mem: 4823
Test:  [69/70]  eta: 0:00:01  model_time: 0.2344 (0.2408)  evaluator_time: 0.0164 (0.0183)  time: 1.4274  data: 1.1546  max mem: 4823
Test: Total time: 0:01:52 (1.6061 s / it)
Averaged stats: model_time: 0.2344 (0.2408)  evaluator_time: 0.0164 (0.0183)
Accumulating evaluation results...
DONE (t=0.15s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.063
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.163
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.041
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.063
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.216
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.316
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.316
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.316
*****Validation*****
Loss: 0.2100 | Classifier Loss: 0.1023 | Box Reg Loss: 0.0922 | RPN Box Reg Loss: 0.0078 | Objectness Loss: 0.0077
creating index...
index created!
Test:  [ 0/18]  eta: 0:00:56  model_time: 0.2481 (0.2481)  evaluator_time: 0.0170 (0.0170)  time: 3.1584  data: 2.8652  max mem: 4823
Test:  [17/18]  eta: 0:00:00  model_time: 0.2232 (0.2304)  evaluator_time: 0.0090 (0.0166)  time: 0.4592  data: 0.1895  max mem: 4823
Test: Total time: 0:00:08 (0.4627 s / it)
Averaged stats: model_time: 0.2232 (0.2304)  evaluator_time: 0.0090 (0.0166)
Accumulating evaluation results...
DONE (t=0.05s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.047
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.126
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.016
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.048
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.159
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.245
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.247
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.247

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Epoch: 2/18 | LR: 0.003000
*****Training*****
Loss: 0.2037 | Classifier Loss: 0.0966 | Box Reg Loss: 0.0956 | RPN Box Reg Loss: 0.0065 | Objectness Loss: 0.0050
creating index...
index created!
Test:  [ 0/70]  eta: 0:08:44  model_time: 0.2610 (0.2610)  evaluator_time: 0.0114 (0.0114)  time: 7.4882  data: 7.1921  max mem: 4823
Test:  [69/70]  eta: 0:00:01  model_time: 0.2194 (0.2324)  evaluator_time: 0.0126 (0.0234)  time: 1.3026  data: 1.0434  max mem: 4823
Test: Total time: 0:01:50 (1.5745 s / it)
Averaged stats: model_time: 0.2194 (0.2324)  evaluator_time: 0.0126 (0.0234)
Accumulating evaluation results...
DONE (t=0.14s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.111
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.248
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.074
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.112
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.426
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.467
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.467
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.467
*****Validation*****
Loss: 0.1875 | Classifier Loss: 0.0861 | Box Reg Loss: 0.0839 | RPN Box Reg Loss: 0.0059 | Objectness Loss: 0.0115
creating index...
index created!
Test:  [ 0/18]  eta: 0:00:47  model_time: 0.2332 (0.2332)  evaluator_time: 0.0089 (0.0089)  time: 2.6638  data: 2.3937  max mem: 4823
Test:  [17/18]  eta: 0:00:00  model_time: 0.2211 (0.2230)  evaluator_time: 0.0111 (0.0161)  time: 0.4192  data: 0.1575  max mem: 4823
Test: Total time: 0:00:07 (0.4235 s / it)
Averaged stats: model_time: 0.2211 (0.2230)  evaluator_time: 0.0111 (0.0161)
Accumulating evaluation results...
DONE (t=0.05s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.107
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.249
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.054
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.108
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.339
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.413
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.413
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.413

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Epoch: 3/18 | LR: 0.003000
*****Training*****
Loss: 0.1790 | Classifier Loss: 0.0856 | Box Reg Loss: 0.0817 | RPN Box Reg Loss: 0.0064 | Objectness Loss: 0.0052
creating index...
index created!
Test:  [ 0/70]  eta: 0:07:27  model_time: 0.3605 (0.3605)  evaluator_time: 0.0294 (0.0294)  time: 6.3919  data: 5.9810  max mem: 4823
Test:  [69/70]  eta: 0:00:01  model_time: 0.2220 (0.2322)  evaluator_time: 0.0141 (0.0181)  time: 1.4244  data: 1.1699  max mem: 4823
Test: Total time: 0:01:51 (1.5920 s / it)
Averaged stats: model_time: 0.2220 (0.2322)  evaluator_time: 0.0141 (0.0181)
Accumulating evaluation results...
DONE (t=0.18s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.144
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.314
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.105
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.144
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.512
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.540
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.540
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.540
*****Validation*****
Loss: 0.1610 | Classifier Loss: 0.0847 | Box Reg Loss: 0.0647 | RPN Box Reg Loss: 0.0053 | Objectness Loss: 0.0062
creating index...
index created!
Test:  [ 0/18]  eta: 0:00:50  model_time: 0.2258 (0.2258)  evaluator_time: 0.0194 (0.0194)  time: 2.8044  data: 2.5273  max mem: 4823
Test:  [17/18]  eta: 0:00:00  model_time: 0.2226 (0.2294)  evaluator_time: 0.0145 (0.0188)  time: 0.4122  data: 0.1410  max mem: 4823
Test: Total time: 0:00:07 (0.4164 s / it)
Averaged stats: model_time: 0.2226 (0.2294)  evaluator_time: 0.0145 (0.0188)
Accumulating evaluation results...
DONE (t=0.05s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.126
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.291
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.100
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.126
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.453
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489

import os
import pandas as pd
import torch
from torchvision.transforms import functional as F
from tqdm.notebook import tqdm
from PIL import Image


class TestDataset:
    def __init__(self, root, info_root, transforms=None):
        self.root = root
        self.transforms = transforms
        self.test_info = pd.read_csv(os.path.join(info_root, "test.csv"))

    def __len__(self):
        return len(self.test_info)

    def __getitem__(self, idx):
        
        file_name = self.test_info.iloc[idx]["Filename"]
        img_path = os.path.join(self.root, "test_images", file_name.replace(".dcm", ".jpg"))  # 確保對應到 .jpg
        if not os.path.exists(img_path):
            raise FileNotFoundError(f"Image file not found: {img_path}")
        image = Image.open(img_path).convert("RGB")
        
        width = self.test_info.iloc[idx]["Width"]
        height = self.test_info.iloc[idx]["Height"]
        
        if self.transforms:
            image = self.transforms(image)
        
        
        return self.test_info.iloc[idx]["ID"], image, width, height


def inference_on_test_set(model, test_loader, device):
    model.eval()
    results = []
    
    with torch.no_grad():
        for img_ids, images, widths, heights in tqdm(test_loader):
            
            images = [img.to(device) for img in images]
            outputs = model(images)  # 得到 [ { 'boxes':..., 'labels':..., 'scores':... }, ... ]
            
            
            for img_id, output, img, w, h in zip(img_ids, outputs, images, widths, heights):
                w, h = float(w), float(h)  
                
                
                for box, label, score in zip(output["boxes"], 
                                             output["labels"], 
                                             output["scores"]):
                    if score < 0.5:  
                        continue
                    
                    
                    category = config.categories[label.item() - 1]
                    
                    
                    if category == "normal":
                        continue
                    
                    
                    xmin, ymin, xmax, ymax = box.tolist()
                    xmin, xmax = xmin / w, xmax / w
                    ymin, ymax = ymin / h, ymax / h
                    
                    
                    results.append({
                        "ID": img_id,
                        "category": category,
                        "score": score.item(),
                        "xmin": xmin,
                        "xmax": xmax,
                        "ymin": ymin,
                        "ymax": ymax
                    })
    return results


def save_results_to_csv(results, output_path):
    df = pd.DataFrame(results, columns=["ID","category","score","xmin","xmax","ymin","ymax"])
    df.to_csv(output_path, index=False)
    print(f"Results saved to {output_path}")


def main():
    
    device = config.device

    
    model = fasterrcnn(num_classes=config.num_classes)
    
    
    checkpoint = torch.load(os.path.join(config.save_root, "final.pth"), map_location=device)
    model.load_state_dict(checkpoint["model"])
    model.to(device)

    
    test_dataset = TestDataset(
        root=config.test_root, 
        info_root=config.info_root_test,
        transforms=F.to_tensor
    )
    
    test_loader = torch.utils.data.DataLoader(
        test_dataset,
        batch_size=1,  
        shuffle=False,
        collate_fn=lambda x: tuple(zip(*x))  
    )

    
    print("Running inference on test set...")
    results = inference_on_test_set(model, test_loader, device)

    
    output_csv = os.path.join(config.save_root, "submission.csv")
    save_results_to_csv(results, output_csv)

if __name__ == "__main__":
    
    class config:
        test_root = "/kaggle/input/dlmia-hw05-test"
        info_root_test = "/kaggle/input/dlmia-hw05-test"
        save_root = "/kaggle/working"
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        num_classes = 8
        categories = [
            "aortic_curvature",
            "aortic_atherosclerosis_calcification",
            "cardiac_hypertrophy",
            "intercostal_pleural_thickening",
            "lung_field_infiltration",
            "degenerative_joint_disease_of_the_thoracic",
            "scoliosis",
            "normal"
        ]
    
    main()

<ipython-input-29-359948ca1d9f>:96: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  checkpoint = torch.load(os.path.join(config.save_root, "final.pth"), map_location=device)

Running inference on test set...

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Results saved to /kaggle/working/submission.csv

import pandas as pd

submission_file = "/kaggle/working/submission.csv"  # 確保此路徑正確
pred_df = pd.read_csv(submission_file)

print("First 10 rows of predictions:")
print(pred_df.head(10))

print("\nLast 10 rows of predictions:")
print(pred_df.tail(10))

print("\nShape of the predictions DataFrame:")
print(pred_df.shape)

First 10 rows of predictions:
                      ID                                    category  \
0  TDR02_20161123_145314        aortic_atherosclerosis_calcification   
1  TDR02_20161123_145314  degenerative_joint_disease_of_the_thoracic   
2  TDR02_20161123_145314                     lung_field_infiltration   
3  TDR02_20161123_145314                            aortic_curvature   
4  TDR01_20171106_111727  degenerative_joint_disease_of_the_thoracic   
5  TDR01_20171106_111727                                   scoliosis   
6  TDR01_20180510_090210  degenerative_joint_disease_of_the_thoracic   
7  TDR01_20180510_090210                                   scoliosis   
8  TDR01_20180511_092549                                   scoliosis   
9  TDR01_20180511_092549  degenerative_joint_disease_of_the_thoracic   

      score      xmin      xmax      ymin      ymax  
0  0.719621  0.522589  0.668645  0.210334  0.404884  
1  0.696984  0.398186  0.679873  0.028602  0.768943  
2  0.687806  0.061978  0.971227  0.036266  0.857390  
3  0.537114  0.396124  0.677786  0.187254  0.753295  
4  0.772386  0.364870  0.653363  0.046136  0.770278  
5  0.610582  0.411904  0.623745  0.196701  0.804504  
6  0.729289  0.371451  0.648256  0.079537  0.846000  
7  0.593864  0.410890  0.618509  0.262055  0.771504  
8  0.592088  0.404281  0.617436  0.168468  0.776159  
9  0.571634  0.362252  0.649401  0.048530  0.820701  

Last 10 rows of predictions:
                        ID                                    category  \
247  TDR02_20161125_122319              intercostal_pleural_thickening   
248  TDR02_20180123_115426              intercostal_pleural_thickening   
249  TDR02_20180123_115426  degenerative_joint_disease_of_the_thoracic   
250  TDR02_20180123_115426                                   scoliosis   
251  TDR02_20180123_115426                     lung_field_infiltration   
252  TDR02_20161118_145330        aortic_atherosclerosis_calcification   
253  TDR02_20161118_145330                     lung_field_infiltration   
254  TDR02_20161118_145330                            aortic_curvature   
255  TDR02_20161118_145330                         cardiac_hypertrophy   
256  TDR02_20161118_145330  degenerative_joint_disease_of_the_thoracic   

        score      xmin      xmax      ymin      ymax  
247  0.638258  0.183890  0.913690  0.062068  0.341851  
248  0.681366  0.163627  0.916379  0.067985  0.391076  
249  0.658080  0.346345  0.680156  0.035037  0.802507  
250  0.654130  0.406345  0.612337  0.193573  0.820105  
251  0.531416  0.025018  0.973885  0.055201  0.895695  
252  0.770725  0.427432  0.595712  0.243232  0.411891  
253  0.751855  0.056390  0.952187  0.066171  0.819678  
254  0.673468  0.297735  0.621747  0.213596  0.688653  
255  0.568339  0.272086  0.780762  0.327880  0.730181  
256  0.543495  0.309093  0.610787  0.041648  0.732416  

Shape of the predictions DataFrame:
(257, 7)

import warnings
warnings.filterwarnings('ignore')

import os
import random
import numpy as np
import pandas as pd

# visualization
import cv2
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
!pip install grad-cam
import pytorch_grad_cam
from pytorch_grad_cam import EigenCAM, AblationCAM
from pytorch_grad_cam.ablation_layer import AblationLayerFasterRCNN
from pytorch_grad_cam.utils.model_targets import FasterRCNNBoxScoreTarget
from pytorch_grad_cam.utils.reshape_transforms import fasterrcnn_reshape_transform
from pytorch_grad_cam.utils.image import show_cam_on_image

# PyTorch
import torch
import torchvision
from torchvision import models
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.transforms import v2

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class config:
    
    root = "/kaggle/input/dlmia-hw05-new"
    num_classes = 8
    categories = ['normal', 'aortic_curvature', 'aortic_atherosclerosis_calcification', 
                  'cardiac_hypertrophy', 'intercostal_pleural_thickening', 'lung_field_infiltration', 
                  'degenerative_joint_disease_of_the_thoracic_spine', 'scoliosis']
    seed = 42
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def seed_everything(seed):
    # Set Python random seed
    random.seed(seed)
    
    # Set NumPy random seed
    np.random.seed(seed)
    
    # Set PyTorch random seed for CPU and GPU
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)
    
    # Set PyTorch deterministic operations for cudnn backend
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = True
    
seed_everything(config.seed)

def fasterrcnn(num_classes):
    model = models.detection.fasterrcnn_resnet50_fpn()
    in_features = model.roi_heads.box_predictor.cls_score.in_features
    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)

    return model

def predict(input_tensor, model, device, detection_threshold):
    outputs = model(input_tensor)
    pred_classes = [config.categories[i] for i in outputs[0]['labels'].cpu().numpy()]
    pred_labels = outputs[0]['labels'].cpu().numpy()
    pred_scores = outputs[0]['scores'].detach().cpu().numpy()
    pred_bboxes = outputs[0]['boxes'].detach().cpu().numpy()
    
    boxes, classes, labels, indices,scores = [], [], [], [], []
    for index in range(len(pred_scores)):
        if pred_scores[index] >= detection_threshold:
            boxes.append(pred_bboxes[index].astype(np.int32))
            classes.append(pred_classes[index])
            labels.append(pred_labels[index])
            indices.append(index)
            scores.append(pred_scores[index])
    boxes = np.int32(boxes)
    
    return boxes, classes, labels, indices, scores

COLORS = np.random.uniform(0, 255, size=(len(config.categories), 3))

def draw_boxes(boxes, labels, classes, image):
    for i, box in enumerate(boxes):
        # Convert RGB to BGR for OpenCV
        color = COLORS[labels[i]].astype(int)[::-1]
        
        # Draw the bounding box
        cv2.rectangle(
            image,
            (int(box[0]), int(box[1])),
            (int(box[2]), int(box[3])),
            color.tolist(), 8
        )
        
        # Increase font size and thickness for label
        font_scale = 4 # Increase this value for larger font
        thickness = 10     # Increase thickness for better visibility
        
        # Add class label as text
        cv2.putText(image, classes[i], 
                    (int(box[0]), int(box[1]) - 10),  # Adjust text position
                    cv2.FONT_HERSHEY_SIMPLEX, 
                    font_scale, 
                    color.tolist(), 
                    thickness,
                    lineType=cv2.LINE_AA)
    return image

def get_transform():
    
    transform = v2.Compose(
                [
                    v2.ToImage(), ## Used while using PIL image
                    #v2.ConvertBoundingBoxFormat(tv_tensors.BoundingBoxFormat.XYXY),
                    v2.ToDtype(torch.float32, scale=True),
                    
                ])       
    
    return transform

from torchvision.transforms.v2 import functional as F

def plot_eigen_cam_images(transforms, model, cat, threshold):
    rows, cols = 4, 2
    fig = plt.figure(figsize=(10, 20))  # Adjust figure size
    grid = plt.GridSpec(rows, cols)

    
    best_ckpt = torch.load("/kaggle/working/final.pth", map_location=config.device)
    model.load_state_dict(best_ckpt["model"])
    model.eval().to(config.device)
    target_layers = [model.backbone]

    cam = EigenCAM(
        model, 
        target_layers, 
        reshape_transform=fasterrcnn_reshape_transform
    )

    for i in range(rows * cols):
        
        all_images = os.listdir(os.path.join(config.root, config.categories[i]))        
        image_path = os.path.join(config.root, config.categories[i], all_images[0])
        image = Image.open(image_path).convert("RGB")   
        
        
        input_tensor = transforms(image)
        input_tensor = input_tensor.to(config.device)
        input_tensor = input_tensor.unsqueeze(0)
        image = np.array(image)
        image_float_np = np.float32(image) / 255

        
        boxes, classes, labels, indices, scores = predict(input_tensor, model, config.device, threshold)        
        image = draw_boxes(boxes, labels, classes, image)
        targets = [FasterRCNNBoxScoreTarget(labels=labels, bounding_boxes=boxes)]
        
        
        grayscale_cam = cam(input_tensor, targets=targets)
        grayscale_cam = grayscale_cam[0, :]
        cam_image = show_cam_on_image(image_float_np, grayscale_cam, use_rgb=True)
        image_with_bounding_boxes = draw_boxes(boxes, labels, classes, cam_image)

       
        categories = fig.add_subplot(grid[i])
        categories.set_axis_off()

        gs = gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=grid[i])
        ax = fig.add_subplot(gs[0])
        ax.imshow(image_with_bounding_boxes)
        ax.set_title(f"{config.categories[i]}")
        ax.axis("off")

    
    fig.patch.set_facecolor('white')
    fig.suptitle("EigenCAM Images of 8 categories\n", fontweight='bold', size=16)
    fig.tight_layout()
    fig.subplots_adjust(wspace=0.2, hspace=0.4)  # Add extra space between plots

def plot_ablation_cam_images(transforms, model):
    
    rows, cols = 4, 2
    fig = plt.figure(figsize = (10, 20))
    grid = plt.GridSpec(rows, cols)
    
    best_ckpt = torch.load("/kaggle/working/final.pth", map_location = config.device)
    model.load_state_dict(best_ckpt["model"])
    model.eval().to(config.device)
    target_layers = [model.backbone]
    
    cam = AblationCAM(model,
                      target_layers,
                      reshape_transform = fasterrcnn_reshape_transform,
                      ablation_layer = AblationLayerFasterRCNN(),
                      ratio_channels_to_ablate = 1.0)
    
    for i in range(rows * cols):
        
        all_images = os.listdir(os.path.join(config.root, config.categories[i]))
        image_path = os.path.join(config.root, config.categories[i], all_images[0])
        image = Image.open(image_path).convert("RGB")       
        input_tensor = transforms(image)
        input_tensor = input_tensor.to(config.device)
        input_tensor = input_tensor.unsqueeze(0)
        image = np.array(image)
        image_float_np = np.float32(image) / 255

        boxes, classes, labels, indices, scores = predict(input_tensor, model, config.device, 0)
        image = draw_boxes(boxes, labels, classes, image)
        targets = [FasterRCNNBoxScoreTarget(labels = labels, bounding_boxes = boxes)]
        
        grayscale_cam = cam(input_tensor, targets = targets)
        grayscale_cam = grayscale_cam[0, :]
        cam_image = show_cam_on_image(image_float_np, grayscale_cam, use_rgb = True)
        image_with_bounding_boxes = draw_boxes(boxes, labels, classes, cam_image)
        
        categories = fig.add_subplot(grid[i])
        categories.set_axis_off()
        
        gs = gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec = grid[i])
        
        ax = fig.add_subplot(gs[0])
        ax.imshow(image_with_bounding_boxes)
        ax.set_title(f"{config.categories[i]}")
        ax.axis("off")
        
    fig.patch.set_facecolor('white')
    fig.suptitle("AblationCAM Images of 8 categories\n", fontweight = 'bold', size = 16)
    fig.tight_layout()

result = plot_eigen_cam_images(transforms = get_transform(), model = fasterrcnn(config.num_classes), cat=0, threshold=0)

model = fasterrcnn(config.num_classes)
result_fig = plot_ablation_cam_images(
    transforms = get_transform(),
    model = model
)

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